Weighted system of expressing language information using a compact notation

ABSTRACT

A special notation that extends the notion of IDL by weighted operators. The Weighted IDL or WIDL can be intersected with a language model, for example an n-gram language model or a syntax-based language model. The intersection is carried out by converting the IDL to a graph, and unfolding the graph in a way which maximizes its compactness.

BACKGROUND

Text to text applications include machine translation, automated summarization, question answering, and other similar applications where a machine carries out the function of understanding some kind of input information and generating text. The input information is often “text”, but can be any kind of information that is received and understandable by the machine.

Conventional text to text applications use heterogeneous methods for implementing the generation phase. Machine translation often produces sentences using application-specific decoders that are based on work that was conducted on speech recognition. Automated summarization produces abstracts using task specific strategies.

Text to text applications have struggled with use of generic natural language generation (NLG) systems, because they typically do not have access to the kind of information required by the formalisms of natural language generation systems. For example, natural language generation may require formalisms such as semantic representations, syntactic relations, and lexical dependencies. The formalisms also require information that is obtained from deep semantic relations, as well as shallow semantic relations and lexical dependency relation. Machine systems typically do not have access to deep subject verb or verb object relations.

A number of natural language generation systems are known, including FUF, Nitrogen, HALogen, and Fergus.

The formal language of IDL (Interleave; Disjunction; Lock) was proposed by Mark-Jan Nederhof and Giorgio Satta in their 2004 paper: IDLexpressions: a formalism for representing and parsing finite languages in natural language processing. Journal of Artificial Intelligence Research, 21: 287-317. Using IDL expressions, one can compactly represent word- and phrase-based encoded meanings. Nederhof and Satta also present algorithms for intersecting IDL expressions with non-probabilistic context free grammars.

SUMMARY

A new language for compactly representing large sets of weighted strings is described. The original IDL language of Nederhof and Satta is extended to a weighted form that can be given a probabilistic interpretation. This language is called Weighted IDL (WIDL).

An aspect provides efficient algorithms for intersecting WIDL expressions with ngram and syntax-based language models. It thus enables one to create from the set of strings that are compactly represented by a WIDL expression those that are grammatical according to some external knowledge resources using those ngram- and syntax-based language models.

An aspect describes how WIDL expressions and the above mentioned intersection algorithms can be used in text-to-text natural language applications, such as machine translation and summarization.

According to one aspect of the present system, probability distributions are associated with weighted IDL (“WIDL) operators, to allow weighted IDL expressions to probabilistically represent biases over the entire set of strings that are subsumed by the weighted IDL expression.

The WIDL expressions may be intersected with various language model combinations, while preserving the compactness property of WIDL expressions. The output is a string that is encoded by the input WIDL expression that receives the highest score based on the combination of WIDL and language model scores.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects will now be described in detail with reference to the accompanying drawings, in which:

FIG. 1 shows a block diagram of an application of this system to a machine translation device;

FIG. 2 shows a block diagram of an application of this system to a machine summarization application;

FIG. 3 shows a WIDL graph;

FIG. 4 illustrates a IDL graph and

FIG. 5 a-5 e show cuts of the IDL graph, with

FIG. 6 showing the finite state acceptor corresponding to that graph;

FIG. 7 shows an exemplary flowchart which can be carried out by a processor when choosing from a large set of weighted possible strings the one of highest probability under an ngram and/or syntax-based language model.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary hardware device and its flow, which may execute the operations that are described with reference to the flowcharts. For the application of machine translation (MT), a WIDL expression generation module 101 is assumed to have access to various MT sources 105. The sources may be parallel corpora of multiple language information. Specifically, the sources may include translation memories, probabilistic and non-probabilistic word- and phrase-based dictionaries, glossaries, Internet information, parallel corpora in multiple languages, non-parallel corpora in multiple languages having similar subject matter, and human-created translations. The generation module 101 takes as input a string in a source language L1 and uses the information in 105 to create a WIDL expression in the target language that compactly encodes all possible renderings of the input string in the target language. The WIDL expression is then processed by the WIDL processor 100, i.e., it is intersected with ngram and syntax-based language models in the target language in order to produce the string of highest probability under all models (the WIDL probability; the language model probabilities).

Alternatively, the component that generates a WIDL expression can be used in conjunction with other text-to-text applications. For example, in summarization as shown in FIG. 2, the words and phrases that are part of the WIDL expression may be chosen by another process as the most important words/phrases in the text. A summarization specific module (102) may generate a WIDL expression.

By intersecting this WIDL expression with ngram and syntax-based language models via the same processor 100, one can generate in this instance sentences that are grammatical and likely to contain important information in a document. The same process can be used similarly in any other applications, such as speech recognition, paraphrasing, natural language generation, question answering, or the like—in any application in which one wishes to generate text.

The WIDL processor 100 may be any general purpose computer, and can be effected by a microprocessor, a digital signal processor, or any other processing device that is capable of executing the operations described herein.

The flowcharts described herein can be instructions which are embodied on a machine-readable medium such as a magnetic disc, optical disk, or any other medium that can be read by a computer. Alternatively, the flowchart can be executed by dedicated hardware, or by any known or later discovered processing device.

The present application describes use of weighted IDL operators. These operators use information that is available in many text to text applications to generate available information relevant to those text to text applications. This may include bias for word choice, word order, phrasal combination and the like.

Weighted IDL, as described herein, encodes meaning via words and phrases that are combined using a set of operator types. The operator types may include:

-   A V type operator which handles word choice -   A (•) dot type operator which handles precedence for phrasal     combination -   a lock (X) operator,

The language models 107 can be n-gram and syntax based language models, as shown. In one implementation of an ngram language model, the probability of a string is computed by multiplying the probabilities of each word in the string given n words before it. In one implementation of a syntax-based language model, one computes the probability of a hierarchical structure associated with a string (a syntactic tree) by multiplying the probabilities of creating each of the nodes in the hierarchical structure.

WIDL allows the “language”, which here refers only to the large set of strings being described by the WIDL, to be described using a very compact representation. Input to the model is produced, and the output of the generation process is the string that is encoded by the input expression that has received the highest score based on the combination of WIDL and language model scores.

IDL was described by Nederhoff and Sata, in their 2004 paper entitled “IDL-expressions: a formalism for representing and parsing finite languages in natural langauge processing” Journal of artificial intelligence research, 21:287-317. IDL expressions may be used as an application independent, information-slim representation language for natural language creation. IDL operators handle natural constraints such as word choice and precedents, constructions such as phrasal combination and under-specifications such as word order.

A brief description of IDL as it applies to this embodiment is provided. The concatenation (•)operator uses strings encoded by its argument expressions to obtain concatenated strings that respect the order of the arguments. For example, the concatenation a•b encodes the singleton set {ab}.

The interleave ∥operator interleaves strings encoded by its argument expression. For example, ∥(a.b,c) encodes the sets {cab, acb, abc}

The disjunction operator V allows a choice among the strings that are encoded by its argument expressions. For example, V(a,b) encodes the set {a,b}

The lock operator X takes only one argument, and locks in the strings encoded by its argument expression such that no additional material can be interleaved. For example, ∥(x (a•b), c) encodes the set {cab,abc}.

Consider the following IDL expression: ∥(finally, V(x(the•prisoners), x(the•captives))•were•released)  (1)

The operators define the way the natural language generation works. The concatenation operator captures precedence constraints. For example, in expression 1, the determiner “the” appears before the noun it determines, here “prisoners and captives”. The lock operator X enforces the phrase encoding constraints. Here, the lock operator maintains the phrase “the captives” as a phrase which needs to be used as a whole, as well as maintaining the phrase “the captives”.

The disjunction operator V allows for multiple word phrase choices. That is, the disjunction between “the prisoners” and “the captives” allows either one to be used. The ∥operator allows for word order freedom. That is, word orders can be underspecified at meanings at the representation level. IDL allows many different strings to be encoded in this way, using a very compact representation. For example, expression 1 can encode the strings:

-   Finally the prisoners were released -   The captives finally were released -   The prisoners were finally released

However, the following strings are not part of the language defined by expression 1:

the finally captives were released (this string is disallowed because the lock operator locks the phrase “the captives”)

the prisoners were released (this string is disallowed because the Interleave operator requires all of its arguments to be represented, and this phrase ignores the “finally”)

finally the captives released were (this string violates the order that is imposed by the precedence operator (were • released).

While the above has described IDL expressions in a text form, IDL graphs may be used as an equivalent method of representing the IDL expressions. FIG. 4 shows an IDL graph corresponding to the IDL expression 1 above. The vertex vs is the start vertex, and the vertex ve is the end vertex. Different for Texas bar resulting from different operator expansions. Different vertices are obtained from different operators. The vertices are also ranked, to enforce a higher priority on the processing of the higher ranked vertices. In FIG. 4, the vertices are ranked as either being 1 or 0 (with 0 rankings not shown in FIG. 4).

The graph G(Π) defines a finite language as the set of strings that can be generated from an IDL specific traversal of G(Π) resulting in this traversing the start to the end.

The IDL graphs can also be intersected with finite state acceptors. An advantage of the IDL representation is its compactness; the IDL need not enumerate all possible interleavings, the notation inherently represents these interleavings without stating them all expressly.

WIDL expressions are created from strings using the word choice/disjunction operator V, the concatenation or precedence operator (•), and the locked interleaved operator ∥. The WIDL expressions described herein have probability distributions associated with each V and ∥ operator. These probability distributions are used to induce a probability value to each of the strings encoded under the operators.

A formal definition of the alphabet of WIDL expressions follows Definition 1 Let Σ be some finite alphabet (called input alphabet), let Δ be another, disjoint, finite alphabet (called distribution alphabet), and let ε be a symbol not in Σ. A WIDL-expression over Σ is a string π satisfying one of the following conditions: 1. π = a, with a ∈ Σ ∪ {ε}; 2. π = V_(δ)(π₁, π₂,... ,π_(n)), with δ ∈ Δ, n ≧ 2, and π_(i) a WIDL-expression, 1 ≦ i ≦ n; 3. π = ||_(δ)(π₁, π₂,...,π_(n)), with δ ∈ Δ, n ≧ 2, and π_(i) a WIDL-expression, 1 ≦ i ≦ n; 4. π = π₁ · π₂, with π₁ and π₂ WIDL-expressions

The WIDL operators operate as follows: the concatenation operator (•) uses the strings encoded by its arguments to obtain concatenated strings in the order stated.

The V_(δ0) operator defines disjunction with associated probability distribution δ₀, and allows for a choice among the strings encoded by its argument expressions. The associated probability distribution δ0 is used to induce a probability distribution δ over all the strings. For example, V_(δ0) (a,b), δ0={1:0.8,2:0.2}, encodes the set {a,b}, over which the probability distribution δ is δ(a)=δ0(1)=0.8; δ(b)=δ0(2)=0.2.

The ∥_(δ0) operator represents lock interleaved with associated probability distribution δ0. This interleaves the strings encoded by its argument expressions. This treats the strings of each argument expression as locked expressions, for example atomic expressions. For example, ∥δ0(a b,c), δ0={1 2:0.95, 2 1 ″0.05} encodes the set {abc,cab}, over which δ assigns the probabilitys δ(abc)=δ0(1 2)−0.95, δ(cab)−δ0 (2 1)=0.05.

Each WIDL expression over Σ and Δ has a formal interpretation as a set of strings from Σ and a probability distribution over that set of strings. The definition Σ can be given as follows: Definition 2 Let Σ be some finite alphabet (called input alphabet), let Δ be another, disjoint, finite alphabet (called distribution alphabet), and let σ be a function mapping an WIDL-expression over Σ and Δ into a set of strings in Σ⁺ characterized by a probability distribution δ ∈ Δ, defined as follows: 1. σ(a) = {a},δ(a) = 1,for a ∈ Σ; 2. σ(ε) = {∈},δ(∈) = 1; 3. σ(π₁ · π₂) = {e₁e₂|e₁ ∈ σ(π₁),e₂ ∈ σ(π₂)},δ(e₁e₂) = δ₁(e₁)·δ₂(e₂), where δ₁ and δ₂ are the probability distributions associated with σ(π₁) and σ(π₂), respectively; 4. σ(V_(δ) ₀ (π₁,...,π_(n)))  =  ∪_(i=1) ^(n){e |e  ∈ σ(π₁)},δ(e_(i))  =  p_(i) · δ_(i)(e_(i)), where δ₀ is specified over {1,...,n} as δ₀(i)  =  p_(i), Σ_(i=1) p_(i)  =  1, and δ_(i)s are the probability distributions associated with each σ(π_(i)), 1 ≦ i ≦ n; 5. σ(||_(δ) ₀ (π₁,...,π_(n)))      = ∪_({i) ...i_(n)}∈Perm(n) {e_(i) ₁ ...e_(i) _(n) |e_(i) _(k)    ∈ σ(π_(i) _(k) )},δ(e_(i) ₁ ...e_(i) _(n) )  =  p_(k  ·) δ_(i) ₁ (e_(i) ₁ )...δ_(i) _(n) (e_(i) _(n) ), where i₁...i_(n) ∈ Perm(n) is the k-th permutation of n elements¹, and δ₀ is specified over Perm(n) as δ₀(i₁...i_(n)) = p_(k), Σ_(k=1) ^(n)p_(k) = 1, and δ_(i) _(k)s are the probability distributions associated with each σ(π_(i) _(k) ), 1 ≦ i_(k) ≦ n.

The ∥δ0 in WIDL is a weaker version of IDL's parallel operator, since the lock is probabilistic, rather than absolute.

Consider the following WIDL expression (2) corresponding to the above IDL expression (1) ∥_(δ) ₁ (finally, V_(δ) ₂ (the • prisoners, the • captives), were, released) δ₁={1 2 3 4:0.25, 2 3 1 4:0.53, all other: 0.10} δ₂={1:0.7,2:0.3}  (2)

The language defined by the WIDL expression is weighted in the sense that it has a probability distribution associated with it. Each string in this language has an associated nonnegative real number less than or equal to one. The sum of the probabilities over the entire “language” adds up to one. The following strings, and their associated probabilities, are among those that are encoded by WIDL expression (2):

-   Finally the prisoners were released probability 0.25 times 0.7 equal     0.175

This string encodes the sequence 1234 (probability 0.25) for the probability of ∥δ1. It also encodes choice 1 for the argument of Vδ2 (probability 0.7) Therefore, it receives a probability of 0.25 times 0.7 equals 0.175.

The captives finally were released; probability 0.1 times 0.3 equals 0.03

This string encodes the sequence 2134 for the arguments of ∥δ0, (probability of 0.1) and choice two for the arguments of Vδ2. (Probability of 0.3.)

The prisoners were finally released; probability 0.53 times 0.7 equals 0.371.

This encodes the sequence 2314 for the arguments of ∥, and choice one for the arguments of Vδ2, probability is 0.53 times 0.7 equals 0.371.

WIDL expressions can be expressed in terms of an equivalent WIDL graph. A WIDL graph is denoted by the parameter set or “tuple” (V, E vs, ve, λ, r), where

V and E are finite sets of vertices and edges respectively,

vs and ve are special vertices in V called start vertices and end vertices respectively.

λ is the edge labeling function that maps the edge set E. into the alphabet ΣU {(ε, <δ,> δ, (δ,) δ}

r is the vertex ranking function, mapping V to the set of non-negative integer numbers.

The label ε indicates that an edge does not consume an input signal. The labels <δ,> δ, and (δ,)δ indicate the same as ε, but also indicate that it is the start or end of something that corresponds to a ∥δ or a Vδ operator respectively. This information may be used to handle locked strings with the intended interpretation of the locked interleaved type of operator.

The following is a formalized definition of how a WIDL expression is mapped into its WIDL graph.

Definition 3

Let Σ be a finite alphabet (called input alphabet), let Δ be another, disjoint, finite alphabet (called distribution alphabet), and let j be a non-negative integer number. Each WIDL-expression π over Σ and Δ is associated with a graph γ_(j)(π)=(V, E; υ_(s), υ_(e), λ, r), specified as follows:

-   -   1. if π=a, a∈Σ∪{ξ}, let υ_(s), υ_(e) be new nodes; I define         -   (a) V={υ_(s), υ_(e)},         -   (b) E={(υ_(s), υ_(e))},         -   (c) λ((υ_(s), υ_(e)))=a for a∈Σ and λ((υ_(s), υ_(e)))=ε for             a=ξ,         -   (d) r(υ_(s))=r(υ_(e))=j;     -   2. if π=V_(δ)(π₁, π₂, . . . , π_(n)) with γ_(j)(π_(i))=(V_(i),         E_(i), υ_(i) ₁ _(s), υ_(i) ₁ _(e), λ_(i), r_(i)), 1≦i≦n, let         υ_(s) ₁ , υ_(e) be new nodes; we define         -   (a) V=∪_(i=1) ^(n)V_(i)∪{υ_(s, υ) _(e)},         -   (b) E=∪_(i=1) ^(n)E_(i)∪{(υ_(s), υ_(i,δ))|1≦i≦n}∪{(υ_(i,e),             υ_(e))|1≦i≦n},         -   (c) λ(e)=λ_(i)(e) for e∈E_(i), λ((υ_(s), υ_(i,δ)))=(δ,             λ((υ_(i) ₁ _(e), υ_(e)))=)_(δ), 1≦i≦n,         -   (d) r(υ)=r_(i)(υ) for υ∈V_(i), r(υ_(s))=r(υ_(e))=j, 1≦i≦n;     -   3. if π=∥_(δ)(π₁, π₂, . . . , π_(n)) with γ_(j+1)(π_(i))=(V_(i),         E_(i), υ_(i) ₁ _(s), υ_(i,e), λ_(i), r_(i)), 1≦i≦n, let υ_(s),         υ_(s) _(i) , . . . , υ_(s) _(n) , υ_(e), υ_(e) _(i) , . . . ,         υ_(e) _(n) be new nodes; we define         -   (a) V=∪_(i=1) ^(n)V_(i)∪{υ_(s) ₁ υ_(s) _(i) , . . . , υ_(s)             _(n) ₁ υ_(e), υ_(e) _(i) , . . . , υ_(e) _(n) },         -   (b) E=∪_(i=1) ^(n)E_(i)∪{(υ_(s), υ_(s) _(i) ), (υ_(s) _(i) ;             υ_(i,s)), |1≦i≦n}∪{(υ_(i,e), υ_(e) _(i) ), (υ_(e) _(i)             ,υ_(e)), 1≦i≦n},         -   (c) λ(e)=λ_(i)(e) for e∈E_(i), λ((υ_(s), υ_(s) _(i) ))=<δ,             λ((υ_(s) _(i) , υ_(i,s))=ε, λ((υ_(i,s), υ_(e) _(i) )=ε,             λ((υ_(e) _(i) , υ_(s)))=>_(δ), 1≦i≦n,         -   (d) r(υ)=r_(i)(υ) for υ∈V_(i), r(υ_(s) _(i) )=r(υ_(e) _(i)             )=j+1, r(υ_(s))=r(υ_(e))=j, 1≦i≦n;     -   4. if π=π₁·π₂ with γ_(j)(π_(i))=(V_(i), E_(i), υ_(i) ₁ _(s),         υ_(i,e), λ_(i), r_(i)), 1i∈{1,2}, let υ_(s), υ_(e) be new nodes;         we define         -   (a) V=V₁∪V₂,         -   (b) E=E₁∪E₂∪{(υ_(1,e), υ_(2,s))},         -   (c) λ(e)=λ_(i)(e) for e∈E₁∪E₂, λ((υ_(1,e), υ_(2,s)))=ε,         -   (d) r(υ)=r_(i)(υ) for υ∈V₁∪V₂;             We define γ(π)=γ₀(π). A WIDL-graph is a graph that has the             form γ(π) for some WIDL-expression π over Σ and Δ.

FIG. 3 shows the WIDL graph corresponding to expression 2 above. The vertices vs and ve respectively correspond to the start and end vertices. The other numbered vertices are between them. Vertices 0, 1, 4, 5, 16, 17, 20, 21; and 2, 3, 14, 15, 18, 19, 22 and 23 result from the expansion of the ∥δ1 operator. The vertices 6, 10; and 9, 13 result from the expansion of the Vδ2 operator. FIG. 4 shows how the arguments of the parallel subscript δ1 operator all have rank one. The ranks help ensure that the desired semantics for the parallel operators is preserved.

Each weighted IDL graph has an associate finite weighted language formed of a finite set of strings with associated probability distribution, as its set of strings. An associated probability that can be obtained from a weighted IDL specific traversal of the graph starts from the start vertex and ends at the end vertex. Therefore, a WIDL expression Π and its corresponding WIDL graphs γ(Π) are said to be equivalent, since they generate the same finite weighted language, denoted as Lδ(Π).

The following text describes how to combine/intersect WIDL graphs with ngram and syntax-based language models by

-   -   explaining first how to efficiently intersect IDL expressions         with ngram language models     -   explaining afterwards the changes we need to make to the         proposed algorithms in order to handle WIDL graphs and         syntax-based language models.

An embodiment describes how to combine the IDL-graphs and Finite-State Acceptors. The IDL formalism is linked with the more classical formalism of finite-state acceptors (FSA) (Hopcroft and Ullman, 1979). The FSA representation can naturally encode precedence multiple choice, but it lacks primitives corresponding to the interleave (II) and lock (x) operators. As such, an FSA representation must explicitly enumerate all possible interleavings, which are inherently captured in an IDL representation. This correspondence between implicit and explicit interleavings is handled by the notion of a cut of an IDL-graph G (Π).

The IDL graph shown in FIG. 3 corresponding to the expression 1 above. FIGS. 5 a-5 e show cuts of the IDL graph.

Intuitively, a cut through G(Π)is a set of vertices that can be reached simultaneously when traversing G from the initial node to the final node, following the branches as prescribed by the encoded I, D, and L operators, in an attempt to produce a string in L(Π). More precisely, the initial vertex v8 is considered a cut (FIG. 5(a)). For each vertex in a given cut, a new cut is created by replacing the start vertex of some edge with the end vertex of that edge, observing the following rules:

1) the vertex that is the start of several edges is labeled using the special symbol ├. This is replaced by a sequence of all the end vertices of these edges (for example, v0v2 is a cut derived from v8, (FIG. 6 b).

A mirror rule handles the special symbol which is actually the end of several edges ┤.

2) The vertex that is the start of an edge labeled using vocabulary items or ε is replaced by the end vertex of that edge (for example, v1v2, v0v3, v0v5, v0v6 are cuts derived from v0v2, v0v2, v0v3 and v0v5, respectively as shown in FIGS. 5 c-5 d, but only if the end vertex is not lower ranked any of the vertices already present in the (for example, v1v6 is not a cut that can be derived from v0v6, see FIG. 5(e).

Note that the last part of the second rule restricts set of cuts by using the ranking mechanism. If v1v6 could be a cut, it would imply that “finally” may appear inserted between the words of the locked phrase “the prisoners”.

The IDL formalism is linked with the FSA formalism by providing a mapping from an IDL-graph G(Π) to an acyclic finite-state acceptor A(Π). Because both formalisms are used for representing finite languages, they have comparable representational power. FIG. 6 shows the equivalent finite state acceptor A(Π) corresponding to the IDL graph of FIG. 3. The IDL representation is much more compact. The set of states of A(Π) is the set of cuts of G(Π). The initial state of the finite-state acceptor is the state corresponding to cut vs, and the final states of the finite-state acceptor are the state corresponding to cuts that contain ve.

The following denotes the state of A(Π) by the name of the cut to which it corresponds. A transition labeled α in A(Π) between states [vi′, . . . vk′ . . . v′j] and state [v″I . . . v″k . . . v″j] occurs if there is an edge (v′k, α, v″k in G(Π). For the example in FIG. 3, the transition labeled were between states [v0v16] and [v1v16] occurs because of the edge labeled finally between nodes v0 and v1 in FIG. 5. The two representations G(Π) and A(Π) are equivalent in the sense that the language generated by IDL-graph G(Π) is the same as the language accepted by FSA A(Π).

The conversion from the IDL representation to the FSA representation destroys the compactness property of the IDL formalism, because of the explicit enumeration of all possible interleavings, which causes certain labels to appear repeatedly in transitions. For example, a transition labeled finally appears 11 times in the finite state acceptor in FIG. 6, whereas an edge labeled finally appears only once in the IDL-graph in FIG. 1.

The Computational Properties of IDL-expressions are discussed herein. A first embodiment describes IDL-graphs and Weighted Finite-State acceptors.

As mentioned above, the generation mechanism that concerns this disclosure performs an efficient intersection of IDL expressions with n-gram language models. Following (Mohri et al., 2002; Knight and Graehl, 1998), language models are first implemented using weighted finite-state acceptors (wFSA). The above describes a mapping from an IDL-graph G(Π) to a finite-state acceptor A(Π). This finite-state acceptor A(Π), provides a weighted finite-state acceptor W(Π), by splitting the states of A(Π) according to the information needed by the language model to assign weights to transitions. For example, under a bigram language model LM, state [v1v16] in FIG. 6 is split into three different states, [prisoners, v1v16], [captives, v1v16], and finally, v1v16], according to which (non-epsilon) transition was last used to reach this state. The transitions leaving these states have the same labels as those leaving state [v1V16], and are weighted using the language model probability distributions pLM(.|captives), and pLM(.|prisoners), pLM(.|captives), and PLM (.|finally), respectively.

This point therefore already represents intersecting IDL-expressions with n-gram language models. From an IDL-expression r, following the mapping Π->g(Π)->A(Π)->W(Π), a weighted finite-state acceptor is obtained. This allows using a single-source shortest path algorithm for directed acyclic graphs (Cormen et al., 2001) to extract the realization corresponding to the most probable path.

Premature unfolding of the IDL-graph into a finite-state acceptor can destroy the representation compactness of the IDL representation. For this reason, techniques are dislosed that, although similar in spirit with the single-source shortest-path algorithm for directed acyclic graphs, perform on-the-fly unfolding of the IDL-graph, with a mechanism to control the unfolding based on the scores of the paths already unfolded. Such an approach has the advantage that prefixes that are extremely unlikely under the language model may be regarded as not so promising, and parts of the IDL expression that contain them may not be unfolded. This may lead to savings.

A disclosed technique describes generation via Intersection of IDL-expressions with Language Models Algorithm IDL-NGLM-BFS. This intersects an IDL-graph with an n-gram language model LM using incremental unfolding and breadth-first search.

The pseudocode for the IDL-NGLM-BFS is shown below. IDL-NGLM-BFS(G, LM) 1  active

{[vs^(G)]} 2  flag

1 3  while

4   do unfold

UNFOLDIDLG(active, G) 5    EVALUATENGLM(unfold, LM) 6    if FINALIDLG

7     then flag

0 8    active

unfold 9  return active

This builds a weighted finite state acceptor W corresponding to an IDL-graph G incrementally, by keeping track of a set of active states, called active. The incrementality comes from creating new transitions and states in W originating in these active states, by unfolding the IDL graph G; the set of newly unfolded states is called unfold. The new transitions in W are weighted according to the language model.

If a final state of W is not yet reached, the while loop is closed by making the unfold set of states to be the next set of active states. Note that this is actually a breadth first search (BFS) with incremental unfolding. This technique still unfolds the IDL-graph completely, and therefore may have the potential drawback of lack of compactness.

An interesting contribution of algorithm IDL-NGLM-BFS, however, is its incremental unfolding. If, instead of line 8 in mechanisms to control which unfold states become part of the active state set for the unfolding iteration, additional techniques are obtained.

Technique IDL-NGLM-A* is obtained by modifying line 8, thus obtaining the pseudocode: IDL-NGLM-A*(G, LM) 1 active

{[vs^(G)]} 2 flag

1 3 while flag 4  do

UNFOLDIDLG

5   EVALUATENGLM(unfold, LM) 6   if FINALIDLG(unfold, G) 7    then flag

0 8   for each state in unfold       do PUSH(astarQ, state)     active

POP(astarQ) 9 return active

This uses a priority queue, astarQ, as a control mechanism from which the states from unfold are PUSH-ed, sorted according to an admissible heuristic function (Russell and Norvig, 1995). In the next iteration, active is a singleton set containing the state pop-ed out the top of the priority queue.

IDL-NGLM-BEAM is obtained by modifying line 8 to obtain: IDL-NGLM-BEAM

1  active

{[vs^(G)]} 2  

3  while flag 4   do unfold

UNFOLDIDLG(active, G) 5    EVALUATENGLM(unfold, LM) 6    if FINALIDLG(unfold, G) 7     then flag

0 8    active

BEAMSTATES(unfold, beam) 9  return active

In this embodiment, the unfolding is controlled using a probabilistic beam beam, which, via the BEAMSTATES function, selects as active states only the states in unfold reachable with a probability higher or equal to the current maximum probability times the probability beam beam.

The IDL representation is well suited for computing accurate admissible heuristics under language models. These heuristics are needed by the IDL-NGLM-A*algorithm, and are also employed for pruning by the IDL-NGLM-BEAM algorithm.

For each state S in a weighted finite-state acceptor W corresponding to an IDL-graph G, one can efficiently extract from G—without further unfolding the set ‘of all edge labels that can be used to reach the final states of W. This set of labels, denoted FE s/all, is an overestimation of the set of future events reachable from S, because the labels under the V operators are all considered. From FE S/all and the n-l labels (when using an n-gram language model) recorded in state S the set of label sequences of length n-l is obtained. This set, denoted FCEs, is an (over)estimated set of possible future conditioning events for state S, which has the most cost-efficient future conditioning events for state S. Using FCEs, one needs to extract from FE: the set of most cost-efficient future events from under each v operator. This set, denoted FCEs, is used to arrive at an admissible heuristic for state S under a model LM, using Equation 2a: $\begin{matrix} {{h(S)} = {\sum\limits_{e \in {FE}_{S}}{- {\log\left( {\max\limits_{{ce} \in {FCE}_{S}}{p_{LM}\left( {e\text{❘}{ce}} \right)}} \right)}}}} & \left( {2a} \right) \end{matrix}$

If h*(S) is the true future cost for state S, this guarantees that h(S)<=h*(S)from the way FEs and FCEs are constructed. Note that, as it often happens with admissible heuristics, h(S) can come arbitrarily close to h*(S), by computing increasingly better approximations FCEs of FCE*s. Such approximations, however, require increasingly advanced unfoldings of the IDL-graph G (a complete unfolding of G for state S gives FCEs=FCE*s, and consequently h(S)=h*(S)). It follows that arbitrarily accurate admissible heuristics exist for IDL-expressions, but computing them only requires finding a balance between the time and space requirements for computing better heuristics and the speed-up obtained by using them in the search algorithms.

Formal Properties of IDL-NGLM Algorithms

The following theorem states the correctness of our algorithms, in the sense that they find the maximum path encoded by an IDL-graph under an gram language model.

THEOREM 1

Let K be an IDL-expression, G(Π) its IDL-graph, and W(T) its wFSA under an n-gram language model LM. Algorithms NGLM-BFS and IDL-NGLM-A* find the path of maximum probability under LM. Algorithm IDL-NGLM-BEAM finds the path of maximum probability under LM, if all states in W(T) along this path are selected by its BEAMSTATES function. The proof of the theorem follows directly from the correctness of the BFS and A*search, and from the condition imposed on the beam search.

The next theorem characterizes the run-time complexity of these algorithms, in terms of an input IDL expression Π and its corresponding IDL-graph G(Π) complexity. There are three factors that linearly influence the run-time complexity of these algorithms: a is the maximum number of nodes in G(Π) needed to represent a state in A(Π)−a depends solely on Π; w is the maximum number of nodes in G(Π) needed represent a state in W(Π)−w depends on Π and n, the length of the context used by the n-gram language model; and K is the number of states of W (Π)−K also depends on Π and n. Of these three factors, K is by far the predominant one, and we simply call K the complexity of an IDL-expression. Theorem 2 Let π be an IDL-expression, G(π) its IDL-graph, A(π) its FSA, and W(π) its wFSA under an n-gram language model. Let V[A(π)] be the set of states of A(π), and V[W(π)] the set of states of W(π).  Let also a  =

_(c∈V[A(π)]),  w  − 

_(c∈V[W(π)]), and K  =  |V[W(π)]|. Algorithms IDL-NGLM-BFS and IDL-NGLM-BEAM have run-time complexity O(awK). Algorithm IDL-NGLM-A* has run-time complexity O(awK log K).

These techniques are linear in the complexity of the input IDL-expression (with an additional log factor in the case of A* search due to priority queue management) which may be a significant advantage. Depending on the input IDL-expression, the task addressed can vary in complexity from linear to exponential. That is, for the intersection of an IDL-expression.

Intersection of WIDL-expressions with N-gram Language Models can thus be carried out as described above. The techniques for intersecting IDL-expressions with n-gram language models can be used almost without modification for intersecting WIDL-expressions with n-gram language models. These algorithms operate over IDL-graphs, and both IDL-graphs and WIDLgraphs are the same kind of graphs, over which algorithms operate in the same manner. The only modification to the algorithms presented in (Soricut and Marcu, 2005) consists on replacing line 5 in each of the IDL-NGLM-BFS, IDL-NGLM-A*, and IDL-NGLM-BEAM algorithms with a function evaluating the set of unfold state s under both the language model distributions and the input WIDL-expression distributions: EVALUATEWIDL-NGLM (unfold, G, LM).

The Result is a New Family of Algorithms:

WIDL-NGLM-BFS, WIDL-NGLM-A*, and WIDL-NGLM-BEAM, which perform intersection between the ranked finite languages of WIDL-expressions and n-gram language models. Similar with the IDL representation, accurate admissible heuristics can be computed for WIDLexpressions as well. In fact, Equation (2) above computes also an admissible heuristic for WIDL-expressions as well, as it considers the WIDL costs to be 0 (an underestimation) for each state in the weighted finite state acceptor. More accurate admissible heuristics, however, can be computed, and the result is lower degree of unfolding of the WIDL-expressions and faster search.

The intersection of IDL and WIDL-expressions with syntax-based language models is performed using a similar approach. A (W)IDL-expression is first mapped into a (W)IDL-graph, which is unfolded on-the-fly into a weighted finite state acceptor. Admissible heuristics are used to compute cost estimates for each state created in the finite state acceptor. The cost estimates are used in deciding what parts of the (W)IDL-graph are unfolded, and in what order. To arrive at the cost estimates, the syntax-based language model is used in evaluating prefix syntax trees, corresponding to the prefix strings unfolded from the (W)IDL-graph, as well as in evaluating the future costs, by estimating the cost of the most probable syntax tree which is consistent with the prefix tree already created. The resulting admissible heuristics are used to create a family of algorithms for intersecting (W)IDL-expressions with syntax-based language models, such as A* search algorithms and probabilistic beam search algorithms. Similar with the algorithms for n-gram language model intersection, the only difference between these types of algorithms consists in the selection of the finite state acceptor states which are used for the next round of on-the-fly unfolding of the WIDL-graph.)

FIG. 7 illustrates a flow chart of the end result. A WIDL expression, shown as 700, along with its associated probabilities, is mapped into a syntactic parse tree 710. A WIDL score is produced from the probability distributions δ1 and δ2. A language generation model score NGLM is produced from evaluating the realized string under the n-gram language model. The SBLM score results from evaluating the syntactic tree under a syntactic language model. In this way, the WIDL expressions can be evaluated using a language model.

Although only a few embodiments have been disclosed in detail above, other modifications are possible, and this disclosure is intended to cover all such modifications, and most particularly, any modification which might be predictable to a person having ordinary skill in the art. For example, other operators, besides those described herein, may be used. Also, while the above describes certain techniques of mapping to and intersecting with the natural language model, other techniques of doing this are contemplated. Moreover, while the above has described, and is predominantly directed to, weighted IDL, it should be understood that many of the techniques described here and be used with other kinds of IDL.

Also, only those claims which use the words “means for” are intended to be interpreted under 35 USC 112, sixth paragraph. Moreover, no limitations from the specification are intended to be read into any claims, unless those limitations are expressly included in the claims. 

1. A method, comprising: defining a compact notation for representing large sets of weighted strings via defined operators that include a first operator that is a probability weighted word choice disjunction operator V that allows for multiple word choices which are probabilistically weighted, a concatenation operator • which forces strings within the language to be in an order stated by the operator, and a probabilistically locked interleaving operator ∥, which probabilistically defines how arguments of the probabilistic operator are presented in a string.
 2. A method as in claim 1, further comprising using said notation for a text to text application, by finding a highest probability string in the set represented by the notation.
 3. A method as in claim 1, further comprising using said notation for a text to text application, by finding a highest probability string in a set represented by the notation under an intersection of said set with a language model.
 4. A method as in claim 3, wherein said language model is an n-gram language model.
 5. A method as in claim 3, wherein said language model is a syntax-based language model.
 6. A method as in claim 1, further comprising converting the expression to a WIDL graph which is a function of beginning and end vertices and edges, a set of vertices and edges, an edge labeling function, and a vertex ranking function.
 7. A method as in claim 6, further comprising intersecting the WIDL graph with a language model.
 8. A method as in claim 7, wherein said language model is an n-gram language model.
 9. A method as in claim 7, wherein said language model is a syntax based language model.
 10. A method as in claim 5, wherein said intersecting comprises forming a graph using said notation, and efficiently unfolding vertices of said graph.
 11. A method as in claim 10, wherein said unfolding comprises mapping the graph to a finite state acceptor.
 12. A method as in claim 9, wherein said efficiently unfolding comprises minimizing a number of parts of the graph that need to be unfolded.
 13. A method as in claim 1, further comprising using said notation to translate a source language sentence into a target language sentence.
 14. A method as in claim 1, further comprising using said notation to automatically summarize a plurality of phrases.
 15. A method as in claim 1, further comprising using said notation for a text to text application.
 16. A method, comprising: defining a language in a notation that compactly represents a plurality of different expressions in the notation, and weights different alternatives according to a probabilistic interpretation; and carrying out a text-to-text natural language application by intersecting said notation with a language model.
 17. A method as in claim 16, wherein said language model is an n-gram language model.
 18. A method as in claim 16, wherein said language model is a syntax-based language model.
 19. A method as in claim 16, wherein said intersecting comprises converting the notation into a graph indicative of the notation, and intersecting the graph with the language model.
 20. A method as in claim 19, wherein said converting comprises minimizing a number of parts of the graph that are unfolded.
 21. A method as in claim 19, wherein said converting comprises labeling edges of the graph corresponding to cuts of the graph.
 22. A method as in claim 19, wherein said converting comprises finding a single source shortest path for a directed graph.
 23. A method as in claim 19, wherein said converting comprises unfolding a graph on the fly scoring unfolded paths, and controlling an amount of unfolding based on scores of previous unfolded parts.
 24. A method as in claim 23, wherein said controlling comprises determining unlikely parts of the path, and preventing further unfolding of said unlikely parts.
 25. A method as in claim 19, wherein said converting comprises incrementally unfolding a path, while maintaining information about unfolded parts.
 26. A method as in claim 25, wherein said information is probabilistic information.
 27. A method as in claim 19, wherein said converting comprises determining an estimate of a heuristic which represents a cost efficient future conditioning event for specified ones of the operators.
 28. A method as in claim 27, wherein said heuristic is used to extract a set of most cost effective future events for each operator.
 29. A method as in claim 19, wherein said graph is intersected with an n-gram type language model by implementing the language model as a weighted finite state acceptor, mapping from the ideal graph to the finite state acceptor, and splitting the states to assign weights to transitions. using a technique that is linear in the complexity of an input expression.
 30. A method as in claim 29, wherein said intersecting comprises finding a realization corresponding to the path of maximum probability.
 31. A method as in claim 29, wherein said intersecting comprises finding a probability of a string by multiplying probabilities of each word in the string given n words before it.
 32. A method as in claim 19, wherein said graph is intersected with a syntax-based language model by unfolding the graph on-the-fly into a finite state acceptor, using heuristics to compute cost estimates for each state in the acceptor, and using the cost estimates to decide what parts of the graph to unfold and in what order.
 33. A method as in claim 31, wherein said obtaining cost estimates comprises evaluating syntax trees.
 34. A method as in claim 32, wherein the probability of a hierarchical structure associated with a string is obtained by multiplying probabilities of creating each of the nodes in the hierarchical structure.
 35. A system, comprising: A computer which operates according to a compact notation for representing large sets of weighted strings via defined operators that include a first operator that is a probability weighted word choice disjunction operator V that allows for multiple word choices which are probabilistically weighted, a concatenation operator • which forces strings within the language to be in an order stated by the operator, and a probabilistically locked interleaving operator ∥, which probabilistically defines how arguments of the probabilistic operator are presented in a string.
 36. A system as in claim 35, wherein said computer uses said notation for a text-to-text application.
 37. A system as in claim 35, wherein said computer also intersects said set with a language model.
 38. A method as in claim 37, wherein said language model is an n-gram language model.
 39. A method as in claim 37, wherein said language model is a syntax-based language model.
 40. A system as in claim 37, wherein said intersects is done by converting the expression to a WIDL graph which is a function of beginning and end vertices and edges, a set of vertices and edges, an edge labeling function, and a vertex ranking function.
 41. A system as in claim 40, wherein said intersecting comprises unfolding the graph, while minimizing a number of parts of the graph that need to be unfolded. 